677 lines
23 KiB
Python
677 lines
23 KiB
Python
import math
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import os
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from dataclasses import dataclass
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from typing import Dict, List, Optional, Tuple
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import torch
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from PIL import Image, ImageOps
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from transformers import (
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AutoProcessor,
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LlamaTokenizerFast,
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PretrainedConfig,
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ProcessorMixin,
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)
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def select_best_resolution(image_size, candidate_resolutions):
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# used for cropping
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original_width, original_height = image_size
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best_fit = None
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max_effective_resolution = 0
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min_wasted_resolution = float("inf")
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for width, height in candidate_resolutions:
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scale = min(width / original_width, height / original_height)
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downscaled_width, downscaled_height = int(original_width * scale), int(
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original_height * scale
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)
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effective_resolution = min(
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downscaled_width * downscaled_height, original_width * original_height
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)
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wasted_resolution = (width * height) - effective_resolution
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if effective_resolution > max_effective_resolution or (
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effective_resolution == max_effective_resolution
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and wasted_resolution < min_wasted_resolution
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):
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max_effective_resolution = effective_resolution
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min_wasted_resolution = wasted_resolution
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best_fit = (width, height)
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return best_fit
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class DictOutput(object):
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def keys(self):
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return self.__dict__.keys()
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def __getitem__(self, item):
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return self.__dict__[item]
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def __setitem__(self, key, value):
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self.__dict__[key] = value
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@dataclass
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class VLChatProcessorOutput(DictOutput):
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input_ids: torch.LongTensor
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target_ids: torch.LongTensor
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images: torch.Tensor
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images_seq_mask: torch.BoolTensor
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images_spatial_crop: torch.LongTensor
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def __len__(self):
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return len(self.input_ids)
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class ImageTransform(object):
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def __init__(
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self,
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mean: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5),
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std: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5),
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normalize: bool = True,
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):
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self.mean = mean
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self.std = std
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self.normalize = normalize
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# only load torchvision.transforms when needed
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try:
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import torchvision.transforms as T
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# FIXME: add version check for gguf
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except ImportError as err:
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raise ImportError(
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"Please install torchvision via `pip install torchvision` to use Deepseek-VL2."
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) from err
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transform_pipelines = [T.ToTensor()]
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if normalize:
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transform_pipelines.append(T.Normalize(mean, std))
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self.transform = T.Compose(transform_pipelines)
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def __call__(self, pil_img: Image.Image):
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x = self.transform(pil_img)
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return x
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class DeepseekVLV2Processor(ProcessorMixin):
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tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
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attributes = ["tokenizer"]
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def __init__(
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self,
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tokenizer: LlamaTokenizerFast,
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candidate_resolutions: Tuple[Tuple[int, int]],
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patch_size: int,
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downsample_ratio: int,
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image_mean: Tuple[float, float, float] = (0.5, 0.5, 0.5),
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image_std: Tuple[float, float, float] = (0.5, 0.5, 0.5),
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normalize: bool = True,
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image_token: str = "<image>",
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pad_token: str = "<｜▁pad▁｜>",
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add_special_token: bool = False,
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sft_format: str = "deepseek",
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mask_prompt: bool = True,
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ignore_id: int = -100,
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**kwargs,
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):
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self.candidate_resolutions = candidate_resolutions
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self.image_size = candidate_resolutions[0][0]
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self.patch_size = patch_size
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self.image_mean = image_mean
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self.image_std = image_std
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self.normalize = normalize
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self.downsample_ratio = downsample_ratio
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self.image_transform = ImageTransform(
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mean=image_mean, std=image_std, normalize=normalize
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)
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self.tokenizer = tokenizer
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# must set this，padding side with make a difference in batch inference
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self.tokenizer.padding_side = "left"
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# add the pad_token as special token to use 'tokenizer.pad_token' and 'tokenizer.pad_token_id'
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if tokenizer.pad_token is None:
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self.tokenizer.add_special_tokens({"pad_token": pad_token})
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# add image token
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image_token_id = self.tokenizer.vocab.get(image_token)
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if image_token_id is None:
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special_tokens = [image_token]
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special_tokens_dict = {"additional_special_tokens": special_tokens}
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self.tokenizer.add_special_tokens(special_tokens_dict)
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self.image_token_id = self.tokenizer.vocab.get(image_token)
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# add five special tokens for grounding-related tasks
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# <|ref|>, <|/ref|>, <|det|>, <|/det|>, <|grounding|>
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special_tokens = ["<|ref|>", "<|/ref|>", "<|det|>", "<|/det|>", "<|grounding|>"]
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special_tokens_dict = {"additional_special_tokens": special_tokens}
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self.tokenizer.add_special_tokens(special_tokens_dict)
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# add special tokens for SFT data
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special_tokens = ["<|User|>", "<|Assistant|>"]
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special_tokens_dict = {"additional_special_tokens": special_tokens}
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self.tokenizer.add_special_tokens(special_tokens_dict)
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self.image_token = image_token
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self.pad_token = pad_token
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self.add_special_token = add_special_token
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self.sft_format = sft_format
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self.mask_prompt = mask_prompt
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self.ignore_id = ignore_id
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super().__init__(
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tokenizer,
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**kwargs,
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)
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def format_messages_v2(self, messages, pil_images, max_req_input_len=-1):
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"""play the role of format_messages_v2 and get_images_info in the last version"""
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tokenized_data = []
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masked_tokenized_data = [] # labels
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images_list = []
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images_seq_mask = []
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images_spatial_crop = []
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image_index = 0
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image_token_cnt = messages.count(self.image_token)
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tokenized_str, images, seq_mask, spatial_crop = self.tokenize_with_images(
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messages,
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pil_images[image_index : image_index + image_token_cnt],
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bos=False,
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eos=True,
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cropping=len(pil_images) <= 2,
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max_req_input_len=max_req_input_len,
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)
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image_index = image_token_cnt
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tokenized_data += tokenized_str
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if self.mask_prompt:
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masked_tokenized_data += [self.ignore_id] * len(tokenized_str)
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else:
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masked_tokenized_data += tokenized_str
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images_list += images
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images_seq_mask += seq_mask
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images_spatial_crop += spatial_crop
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assert len(tokenized_data) == len(
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images_seq_mask
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), f"format_messages_v2: tokenized_str's length {len(tokenized_str)} is not equal to imags_seq_mask's length {len(images_seq_mask)}"
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return (
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tokenized_data,
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masked_tokenized_data,
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images_list,
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images_seq_mask,
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images_spatial_crop,
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)
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@property
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def bos_id(self):
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return self.tokenizer.bos_token_id
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@property
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def eos_id(self):
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return self.tokenizer.eos_token_id
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@property
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def pad_id(self):
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return self.tokenizer.pad_token_id
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def encode(self, text: str, bos: bool = True, eos: bool = False):
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t = self.tokenizer.encode(text, add_special_tokens=False)
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if bos:
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t = [self.bos_id] + t
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if eos:
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t = t + [self.eos_id]
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return t
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def decode(self, t: List[int], **kwargs) -> str:
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return self.tokenizer.decode(t, **kwargs)
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def process_one(
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self,
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prompt: str = None,
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conversations: List[Dict[str, str]] = None,
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images: List[Image.Image] = None,
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apply_sft_format: bool = False,
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inference_mode: bool = True,
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system_prompt: str = "",
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max_req_input_len: int = -1,
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**kwargs,
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):
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"""
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Args:
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prompt (str): the formatted prompt;
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conversations (List[Dict]): conversations with a list of messages;
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images (List[ImageType]): the list of images;
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apply_sft_format (bool): if prompt is not None, then apply the SFT format to prompt;
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if conversations is not None, then it will always apply the SFT format to conversations;
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inference_mode (bool): if True, then remove the last eos token;
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system_prompt (str): the system prompt;
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**kwargs:
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Returns:
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outputs (BaseProcessorOutput): the output of the processor,
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- input_ids (torch.LongTensor): [N + image tokens]
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- target_ids (torch.LongTensor): [N + image tokens]
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- images (torch.FloatTensor): [n_images, 3, H, W]
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- image_id (int): the id of the image token
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- num_image_tokens (List[int]): the number of image tokens
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"""
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assert (
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prompt is None or conversations is None
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), "prompt and conversations cannot be used at the same time."
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(
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tokenized_str,
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masked_tokenized_str,
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images_list,
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images_seq_mask,
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images_spatial_crop,
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) = self.format_messages_v2(conversations, images, max_req_input_len)
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assert (
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len(tokenized_str) == len(images_seq_mask) == len(masked_tokenized_str)
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), (
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f"tokenized_str's length {len(tokenized_str)}, input_ids' length {len(masked_tokenized_str)}, "
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f"imags_seq_mask's length {len(images_seq_mask)}, are not equal"
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)
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input_ids = torch.LongTensor(tokenized_str)
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target_ids = torch.LongTensor(masked_tokenized_str)
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images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
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# set input_ids < 0 | input_ids == self.image_token_id as ignore_id
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target_ids[(input_ids < 0) | (input_ids == self.image_token_id)] = (
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self.ignore_id
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)
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input_ids[input_ids < 0] = self.pad_id
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if inference_mode:
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assert input_ids[-1] == self.eos_id
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input_ids = input_ids[:-1]
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target_ids = target_ids[:-1]
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images_seq_mask = images_seq_mask[:-1]
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if len(images_list) == 0:
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images = torch.zeros((1, 3, self.image_size, self.image_size))
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images_spatial_crop = torch.zeros((1, 2), dtype=torch.long)
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else:
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images = torch.stack(images_list, dim=0)
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images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
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prepare = VLChatProcessorOutput(
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input_ids=input_ids,
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target_ids=target_ids,
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images=images,
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images_seq_mask=images_seq_mask,
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images_spatial_crop=images_spatial_crop,
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)
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return prepare
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def __call__(
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self,
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*,
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prompt: str = None,
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conversations: List[Dict[str, str]] = None,
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images: List[Image.Image] = None,
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apply_sft_format: bool = False,
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inference_mode: bool = True,
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system_prompt: str = "",
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max_req_input_len: int = -1,
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**kwargs,
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):
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prepare = self.process_one(
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prompt=prompt,
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conversations=conversations,
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images=images,
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apply_sft_format=apply_sft_format,
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inference_mode=inference_mode,
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system_prompt=system_prompt,
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max_req_input_len=max_req_input_len,
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)
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return prepare
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def find_all_indices(self, messages, target_value):
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indices = []
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for index, item in enumerate(messages):
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if item == target_value:
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indices.append(index)
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return indices
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def tokenize_with_images(
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self,
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conversation: str,
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images: List[Image.Image],
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bos: bool = True,
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eos: bool = True,
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cropping: bool = True,
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max_req_input_len: int = -1,
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):
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"""Tokenize text with <image> tags."""
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images_list, images_seq_mask, images_spatial_crop = [], [], []
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text_splits = conversation.split(self.image_token)
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tokenized_str = []
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for text_sep, image in zip(text_splits, images):
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"""encode text_sep"""
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tokenized_sep = self.encode(text_sep, bos=False, eos=False)
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tokenized_str += tokenized_sep
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images_seq_mask += [False] * len(tokenized_sep)
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"""select best resolution for anyres"""
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if cropping:
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best_width, best_height = select_best_resolution(
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image.size, self.candidate_resolutions
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)
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else:
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best_width, best_height = self.image_size, self.image_size
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# print(image.size, (best_width, best_height)) # check the select_best_resolutions func
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"""process the global view"""
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global_view = ImageOps.pad(
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image,
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(self.image_size, self.image_size),
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color=tuple(int(x * 255) for x in self.image_transform.mean),
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)
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images_list.append(self.image_transform(global_view))
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"""process the local views"""
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local_view = ImageOps.pad(
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image,
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(best_width, best_height),
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color=tuple(int(x * 255) for x in self.image_transform.mean),
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)
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for i in range(0, best_height, self.image_size):
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for j in range(0, best_width, self.image_size):
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images_list.append(
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self.image_transform(
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local_view.crop(
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(j, i, j + self.image_size, i + self.image_size)
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)
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)
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)
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"""record height / width crop num"""
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num_width_tiles, num_height_tiles = (
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best_width // self.image_size,
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best_height // self.image_size,
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)
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images_spatial_crop.append([num_width_tiles, num_height_tiles])
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"""add image tokens"""
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h = w = math.ceil(
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(self.image_size // self.patch_size) / self.downsample_ratio
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)
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# global views tokens h * (w + 1), 1 is for line seperator
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tokenized_image = [self.image_token_id] * h * (w + 1)
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# add a seperator between global and local views
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tokenized_image += [self.image_token_id]
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# local views tokens, (num_height_tiles * h) * (num_width_tiles * w + 1)
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tokenized_image += (
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[self.image_token_id]
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* (num_height_tiles * h)
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* (num_width_tiles * w + 1)
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)
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tokenized_str += tokenized_image
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images_seq_mask += [True] * len(tokenized_image)
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# print(width_crop_num, height_crop_num, len(tokenized_image)) # test the correctness of the number of image-related tokens
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"""process the last text split"""
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tokenized_sep = self.encode(text_splits[-1], bos=False, eos=False)
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# deal with video, limit with request len
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if max_req_input_len > -1:
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if max_req_input_len < len(tokenized_sep) + len(tokenized_str) - 1:
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rest = max_req_input_len - len(tokenized_sep) - 1 - 1024
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tokenized_str = tokenized_str[:rest]
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images_seq_mask = images_seq_mask[:rest]
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tokenized_str += tokenized_sep
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images_seq_mask += [False] * len(tokenized_sep)
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"""add the bos and eos tokens"""
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if bos:
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tokenized_str = [self.bos_id] + tokenized_str
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images_seq_mask = [False] + images_seq_mask
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if eos:
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tokenized_str = tokenized_str + [self.eos_id]
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images_seq_mask = images_seq_mask + [False]
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assert len(tokenized_str) == len(
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images_seq_mask
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), f"tokenize_with_images func: tokenized_str's length {len(tokenized_str)} is not equal to imags_seq_mask's length {len(images_seq_mask)}"
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return tokenized_str, images_list, images_seq_mask, images_spatial_crop
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class DeepseekVL2VisionEncoderConfig(PretrainedConfig):
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model_type: str = "vision"
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model_name: str = "siglip_large_patch16_384"
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image_size: int = 384
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patch_size: int = 16
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width: int = 1024
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layers: int = 24
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heads: int = 16
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mlp_ratio: int = 4
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global_pool: str = "map"
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ignore_head: bool = True
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class_token: bool = False
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num_classes: int = 0
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use_checkpoint: bool = False
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weight_init: str = "skip"
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deterministic: bool = False
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num_recomputing_layers: int = 0
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def __init__(
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self,
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model_name: str = "siglip_large_patch16_384",
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image_size: int = 384,
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patch_size: int = 16,
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width: int = 1024,
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layers: int = 24,
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heads: int = 16,
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mlp_ratio: int = 4,
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global_pool: str = "map",
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ignore_head: bool = True,
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class_token: bool = False,
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num_classes: int = 0,
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use_checkpoint: bool = False,
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**kwargs,
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):
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self.model_name = model_name
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self.image_size = image_size
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self.patch_size = patch_size
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self.width = width
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self.layers = layers
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self.heads = heads
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self.mlp_ratio = mlp_ratio
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self.global_pool = global_pool
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self.ignore_head = ignore_head
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self.class_token = class_token
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self.num_classes = num_classes
|
||
self.use_checkpoint = use_checkpoint
|
||
|
||
super().__init__(**kwargs)
|
||
|
||
|
||
class DeepseekVL2MlpProjectorConfig(PretrainedConfig):
|
||
model_type = "mlp_projector"
|
||
projector_type: str = "downsample_mlp_gelu"
|
||
input_dim: int = 1152
|
||
n_embed: int = 2048
|
||
depth: int = 2
|
||
mlp_ratio: int = 1
|
||
downsample_ratio: int = 2
|
||
token_pooling: bool = False
|
||
|
||
def __init__(
|
||
self,
|
||
projector_type: str = "downsample_mlp_gelu",
|
||
input_dim: int = 1152,
|
||
n_embed: int = 2048,
|
||
depth: int = 2,
|
||
mlp_ratio: int = 1,
|
||
downsample_ratio: int = 2,
|
||
**kwargs,
|
||
):
|
||
self.projector_type = projector_type
|
||
self.input_dim = input_dim
|
||
self.n_embed = n_embed
|
||
self.depth = depth
|
||
self.mlp_ratio = mlp_ratio
|
||
self.downsample_ratio = downsample_ratio
|
||
|
||
super().__init__(**kwargs)
|
||
|
||
|
||
class DeepseekV2Config(PretrainedConfig):
|
||
|
||
model_type = "deepseek_v2"
|
||
keys_to_ignore_at_inference = ["past_key_values"]
|
||
|
||
def __init__(
|
||
self,
|
||
vocab_size=102400,
|
||
hidden_size=4096,
|
||
intermediate_size=11008,
|
||
moe_intermediate_size=1407,
|
||
num_hidden_layers=30,
|
||
num_attention_heads=32,
|
||
num_key_value_heads=32,
|
||
n_shared_experts=None,
|
||
n_routed_experts=None,
|
||
ep_size=1,
|
||
routed_scaling_factor=1.0,
|
||
kv_lora_rank=512,
|
||
q_lora_rank=1536,
|
||
qk_rope_head_dim=64,
|
||
v_head_dim=128,
|
||
qk_nope_head_dim=128,
|
||
topk_method="gready",
|
||
n_group=None,
|
||
topk_group=None,
|
||
num_experts_per_tok=None,
|
||
moe_layer_freq=1,
|
||
first_k_dense_replace=0,
|
||
norm_topk_prob=False,
|
||
scoring_func="softmax",
|
||
aux_loss_alpha=0.001,
|
||
seq_aux=True,
|
||
hidden_act="silu",
|
||
max_position_embeddings=2048,
|
||
initializer_range=0.02,
|
||
rms_norm_eps=1e-6,
|
||
use_cache=True,
|
||
pad_token_id=None,
|
||
bos_token_id=100000,
|
||
eos_token_id=100001,
|
||
pretraining_tp=1,
|
||
tie_word_embeddings=False,
|
||
rope_theta=10000.0,
|
||
rope_scaling=None,
|
||
attention_bias=False,
|
||
attention_dropout=0.0,
|
||
use_mla=True,
|
||
**kwargs,
|
||
):
|
||
self.vocab_size = vocab_size
|
||
self.max_position_embeddings = max_position_embeddings
|
||
self.hidden_size = hidden_size
|
||
self.intermediate_size = intermediate_size
|
||
self.moe_intermediate_size = moe_intermediate_size
|
||
self.num_hidden_layers = num_hidden_layers
|
||
self.num_attention_heads = num_attention_heads
|
||
self.n_shared_experts = n_shared_experts
|
||
self.n_routed_experts = n_routed_experts
|
||
self.ep_size = ep_size
|
||
self.routed_scaling_factor = routed_scaling_factor
|
||
self.kv_lora_rank = kv_lora_rank
|
||
self.q_lora_rank = q_lora_rank
|
||
self.qk_rope_head_dim = qk_rope_head_dim
|
||
self.v_head_dim = v_head_dim
|
||
self.qk_nope_head_dim = qk_nope_head_dim
|
||
self.topk_method = topk_method
|
||
self.n_group = n_group
|
||
self.topk_group = topk_group
|
||
self.num_experts_per_tok = num_experts_per_tok
|
||
self.moe_layer_freq = moe_layer_freq
|
||
self.first_k_dense_replace = first_k_dense_replace
|
||
self.norm_topk_prob = norm_topk_prob
|
||
self.scoring_func = scoring_func
|
||
self.aux_loss_alpha = aux_loss_alpha
|
||
self.seq_aux = seq_aux
|
||
# for backward compatibility
|
||
if num_key_value_heads is None:
|
||
num_key_value_heads = num_attention_heads
|
||
|
||
self.num_key_value_heads = num_key_value_heads
|
||
self.hidden_act = hidden_act
|
||
self.initializer_range = initializer_range
|
||
self.rms_norm_eps = float(rms_norm_eps)
|
||
self.pretraining_tp = pretraining_tp
|
||
self.use_cache = use_cache
|
||
self.rope_theta = rope_theta
|
||
self.rope_scaling = rope_scaling
|
||
self.attention_bias = attention_bias
|
||
self.attention_dropout = attention_dropout
|
||
self.use_mla = use_mla
|
||
|
||
super().__init__(
|
||
pad_token_id=pad_token_id,
|
||
bos_token_id=bos_token_id,
|
||
eos_token_id=eos_token_id,
|
||
tie_word_embeddings=tie_word_embeddings,
|
||
**kwargs,
|
||
)
|
||
|
||
|
||
class DeepseekVL2Config(PretrainedConfig):
|
||
model_type = "deepseek_vl_v2"
|
||
vision_config: DeepseekVL2VisionEncoderConfig
|
||
projector_config: DeepseekVL2MlpProjectorConfig
|
||
language_config: DeepseekV2Config
|
||
|
||
tile_tag: str = "2D"
|
||
global_view_pos: str = "head"
|
||
candidate_resolutions: Tuple[Tuple[int, int]] = ((384, 384),)
|
||
|
||
def __init__(
|
||
self,
|
||
tile_tag: str = "tile_tag",
|
||
global_view_pos: str = "head",
|
||
candidate_resolutions: Tuple[Tuple[int, int]] = ((384, 384),),
|
||
**kwargs,
|
||
):
|
||
super().__init__(**kwargs)
|
||
|
||
vision_config = kwargs.get("vision_config", {})
|
||
self.vision_config = DeepseekVL2VisionEncoderConfig(**vision_config)
|
||
|
||
projector_config = kwargs.get("projector_config", {})
|
||
self.projector_config = DeepseekVL2MlpProjectorConfig(**projector_config)
|
||
|
||
language_config = kwargs.get("language_config", {})
|
||
if isinstance(language_config, DeepseekV2Config):
|
||
self.language_config = language_config
|
||
else:
|
||
self.language_config = DeepseekV2Config(**language_config)
|
||
|
||
self.tile_tag = tile_tag
|
||
self.global_view_pos = global_view_pos
|
||
self.candidate_resolutions = candidate_resolutions
|
||
self.architectures = ["DeepseekVL2ForCausalLM"]
|
||
|
||
|
||
AutoProcessor.register(DeepseekVL2Config, DeepseekVLV2Processor)
|